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Published By: SAS     Published Date: Mar 14, 2014
Managing expectations before, during and after the adoption of visualization software is crucial. Users should know what the rollout process will look like and how it will take place, and have clear goals for using the tool. Make sure that the desired outcome isn’t just look-and-feel. Creating beautiful charts and graphs is not a substitute for practical business decisions.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics
    
SAS
Published By: SAS     Published Date: Nov 10, 2014
Learn how to build a business intelligence product that provides self-service analytics and see why this product has been the favorite for businesses and IT users alike.
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sas, self service, service analytics, it users, self-service bi, forecasting, collaboration, deployment, visual analytics
    
SAS
Published By: SAS     Published Date: Apr 21, 2015
To maintain competitive advantage, your BI solution should allow business users to quickly and easily investigate and interrogate the data to find out why something happened – to uncover the root cause behind the “what.”
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SAS
Published By: SAS     Published Date: Nov 04, 2015
If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis.
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data visualization, sas, big data, visual analytics, data exploration, analysis
    
SAS
Published By: SAS     Published Date: May 12, 2016
This paper examines the barriers to adoption from an IT and end-user perspective, and shows how self-service analytics in general – and SAS Visual Analytics in particular – can eliminate these barriers. Self-service analytics empowers users to truly exploit the wealth of data available to them, while ensuring that the IT organization maintains governance and control over that data.
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sas, business analytics, it organization
    
SAS
Published By: SAS     Published Date: Aug 03, 2016
No matter the vintage or sophistication of your organization’s data warehouse (DW) and the environment around it, it probably needs to be modernized in one or more ways. That’s because DWs and requirements for them continue to evolve. Many users need to get caught up by realigning the DW environment with new business requirements and technology challenges. Once caught up, they need a strategy for continuous modernization.
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best practices, technology, data, business technology
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
But if you can’t explain how you got the answer, or what it means, it’s no good. Most self-service BI solutions can only display what has already happened, through reports or dashboards. And most have a predefined path of analysis that gives users very little creative freedom to explore new lines of thought. To maintain competitive advantage, your BI solution should allow business users to quickly and easily investigate and interrogate the data to find out why something happened – to uncover the root cause behind the “what.”
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SAS
Published By: SAS     Published Date: Apr 25, 2017
If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis. The heart and soul of SAS Visual Analytics is the SAS® LASR™ Analytic Server, which ca
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SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: May 04, 2017
Should you modernize with Hadoop? If your goal is to catch, process and analyze more data at dramatically lower costs, the answer is yes. In this e-book, we interview two Hadoop early adopters and two Hadoop implementers to learn how businesses are managing their big data and how analytics projects are evolving with Hadoop. We also provide tips for big data management and share survey results to give a broader picture of Hadoop users. We hope this e-book gives you the information you need to understand the trends, benefits and best practices for Hadoop.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: SAS     Published Date: Oct 18, 2017
With the support of SAS, the Internet of Things Institute developed the 2017 Internet of Things ROI Research Study to gather real-world insights, lessons learned and future guidance from current users of IoT technology and advanced analytics. This selective sample of IoT users offers valuable insights to both IoT innovators and organizations still waiting to see how the technology evolves before investing. Multiple business layers and functions have input into IoT decision making. Discover which layer was most critical to success for those organizations that have achieved the highest percentage of their targeted ROI. Learn about the main drivers of success for IoT users achieving higher returns. Lastly, the results highlight six primary factors that can undermine an IoT initiative and how they can be prevented.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
A picture is worth a thousand words – especially when you are trying to find relationships and understand your data – which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your data. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and analytics. And you can do it through an interface that anyone can use. At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days. Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
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SAS
Published By: SAS     Published Date: Mar 06, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
Published By: SAS     Published Date: May 24, 2018
Constant market shifts and changing customer preferences add to the challenge of outperforming your competitors and surpassing stakeholder expectations. But what can be done to steer your organization down the path to greater success? By now, we all know it’s not just historical reporting about the past that will provide the answers needed to drive a business forward. Everyone – from executives and analysts to frontline staff – must have access to insights about the future that will enable them to make the best decisions and take the actions needed to keep their organizations agile. This means the ability to peer into data, explore it, understand it, analyze it and produce insights that provide those aha moments and take actions on it. Such things cannot be done with multiple tools that are rigid, limiting and difficult to use. A new breed of business intelligence is required. Gone are the days when reports looked at singular issues, took possibly days or weeks to create, and required
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SAS
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: SAS     Published Date: Nov 16, 2018
Instances of fraud have become commonplace in many industries, and it’s costing organizations millions of dollars annually and placing corporate brands at risk. But despite significant investments in staff and anti-fraud software, new and emerging fraud threats keep slipping through undetected. Why? Because it’s too costly to keep anti-fraud software current. Paying a vendor to make software changes dramatically increases total cost of ownership. This paper discusses how SAS addresses this challenge with next-generation fraud solutions like SAS Visual Investigator, a cloud-ready investigation and incident management platform that end users can easily configure and adapt to detect new and evolving types of fraud.
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SAS
Published By: SAS     Published Date: Dec 20, 2018
Think of the self-service things you use in a day. Gas pumps. ATMs. Online apps for shopping. They’re convenient and easy to use. People choose what they want, when they want – without involving others in their minute-to-minute decisions. What if your organization could treat data discovery and analytics the same way? SAS has combined two of its visual solutions to do just that. SAS Visual Analytics and SAS Visual Statistics share the same web-based interface to provide self-service data exploration and easy-to-use interactive predictive analytics in a collaborative environment. This white paper takes a look at this convergence and outlines how these products can be used together so that everyone, even nontechnical users, can investigate data on their own, create analytical models and uncover new insights that drive competitive differentiation. Your analytics journey just got a lot easier.
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SAS
Published By: SAS     Published Date: Jan 04, 2019
How can you open your analytics program to all types of programming languages and all levels of users? And how can you ensure consistency across your models and your resulting actions no matter where they initiate in the company? With today’s analytics technologies, the conversation about open analytics and commerical analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics technology systems within your organization. As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Within that cycle, there’s a role for open source and commercial analytics. For example, machine learning algorithms can be developed in SAS or Python, then deployed in real-time data streams within SAS Event Stream Processing, while also integrating with open systems through Java and C APIs, RESTful web services, Apache Kafka, HDFS and more.
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SAS
Published By: Sauce Labs     Published Date: May 30, 2018
In an age where your users demand no-fail experience, continuous testing has become a mission critical component for engineering teams of all sizes. However, while this topic was once discussed at lower levels, the conversation has made it all the way to the C-suite. No matter your industry, if your team isn’t thinking about testing at a high level, then there is a chance that you are missing out on revenue due to flawed app functionality, delayed releases and slowed innovation. It is important to understand the business benefits of continuous testing and automation to avoid these outcomes, and make the changes necessary to set your applications up for success.
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Sauce Labs
Published By: Scalebase     Published Date: Feb 19, 2013
This white paper examines how to scale MySQL databases to handle more users, more connections and more data without re-writing apps or re-architecting the database.
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mysql, databases, users, data distribution, improve, performance
    
Scalebase
Published By: Scalebase     Published Date: Mar 08, 2013
Learn how to scale MySQL databases with ScaleBase. Cost-effectively scale to an infinite number of users, with NO disruption to your existing infrastructure.
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mysql, databases with scalebase, no existing infrastructure issues, use mysql with scalebase, cost effectively scale, shard, cluster, high availability, failover, mariadb, read/write, scalability, capacity planning
    
Scalebase
Published By: Scalebase     Published Date: Mar 08, 2013
Learn how Mozilla is able to cost-effectively scale out their new Firefox mobile operating system App Store (running MySQL) to an infinite number of users, while increasing performance.
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mozilla, scalebase, performance, app store, increasing performance, infinite number of users, increasing performance, shard, cluster, high availability, failover, mariadb, mysql, read/write, scalability, capacity planning
    
Scalebase
Published By: ScanLife     Published Date: Feb 25, 2012
The report details information such as overall barcode scanning trends, mobile operating systems scanning shares, representative demographics of users, and activities of overall states and/or countries with the highest scanning utilization.
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scanlife, marketing, emerging marketing, mobile marketing, barcode, mobile phones, smart phones, interactive marketing, web analytics, business intelligence, market research, usability
    
ScanLife
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